Response Validity

In some cases, an LLM might fail to generate a response due to reasons like limited knowledge or the asked question not being clear. Response Validity score can be used to identify these cases, where a model is not generating an informative response. Columns required:

question: The question asked by the user
response: The response given by the model

How to use it?

from uptrain import EvalLLM, Evals

OPENAI_API_KEY = "sk-********************"  # Insert your OpenAI key here

data = [{
    "question": "What is the chemical formula of chlorophyll?",
    "response": "Sorry, I don't have information about that."
}]

eval_llm = EvalLLM(openai_api_key=OPENAI_API_KEY)

res = eval_llm.evaluate(
    data = data,
    checks = [Evals.VALID_RESPONSE]
)

By default, we are using GPT 3.5 Turbo for evaluations. If you want to use a different model, check out this tutorial.

Sample Response:

[
   {
      "score_valid_response": 0.0,
      "explanation_valid_response": "Step 1: Identify the question and response.\nQuestion: What is the chemical formula of chlorophyll?\nResponse: Sorry, I don't have information about that.\n\nStep 2: Analyze the response.\nThe response states that the AI assistant does not have information about the chemical formula of chlorophyll. It does not provide any specific information about the chemical formula.\n\nStep 3: Evaluate the response based on the given criteria.\nThe response does not contain any information about the chemical formula of chlorophyll. It simply states the lack of information.\n\nConclusion:\nThe given response does not contain any information.\n\n[Choice]: B\n[Explanation]: The response does not contain any information about the chemical formula of chlorophyll. Therefore, the selected choice is B."
   }
]

A higher response validity score reflects that the generated response is valid.

The response generated is not considered to be a valid response as the model has not generated any information on the quesktion asked i.e. “What is the formula of chlorophyll” Rather the generated response is an argument reflecting the model’s inability to generate information. Resulting in a low response validity score.

How it works?

We evaluate response validity by determining which of the following two cases apply for the given task data:

The given response does contain some information.
The given response does not contain any information.

Tutorial

Open this tutorial in GitHub

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Response Relevance Response Consistency

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Pre-configured Evaluations

Supported LLMs

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Response Validity

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